LinL: lost in n-best list

Authors:
Peng Meng;Yun-Qing Shi;Liusheng Huang;Zhili Chen;Wei Yang;Abdelrahman Desoky
Affiliations:
NHPCC, Depart. of CS. & Tech., USTC, Hefei, China and New Jersey Institute of Technology, Newark, New Jersey;New Jersey Institute of Technology, Newark, New Jersey;NHPCC, Depart. of CS. & Tech., USTC, Hefei, China and Suzhou Institute for Advanced Study, USTC, Suzhou, China;NHPCC, Depart. of CS. & Tech., USTC, Hefei, China and Suzhou Institute for Advanced Study, USTC, Suzhou, China;NHPCC, Depart. of CS. & Tech., USTC, Hefei, China and Suzhou Institute for Advanced Study, USTC, Suzhou, China;CSEE, University of Maryland, Baltimore County, MD
Venue:
IH'11 Proceedings of the 13th international conference on Information hiding
Year:
2011

Citing 17
Cited 1

Hiding the Hidden: A software system for concealing ciphertext as innocuous text

ICICS '97 Proceedings of the First International Conference on Information and Communication Security
Steganalysis of JPEG Images: Breaking the F5 Algorithm

IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Lost in just the translation

Proceedings of the 2006 ACM symposium on Applied computing
A Statistical Algorithm for Linguistic Steganography Detection Based on Distribution of Words

ARES '08 Proceedings of the 2008 Third International Conference on Availability, Reliability and Security
Effective Linguistic Steganography Detection

CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Nostega: A Novel Noiseless Steganography Paradigm

Journal of Digital Forensic Practice
Disappearing Cryptography: Information Hiding: Steganography & Watermarking

Disappearing Cryptography: Information Hiding: Steganography & Watermarking
Translation-based steganography

Journal of Computer Security - Best papers of the Sec Track at the 2006 ACM Symposium
Exploiting N-best hypotheses for SMT self-enhancement

HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Moses: open source toolkit for statistical machine translation

ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Listega: list-based steganography methodology

International Journal of Information Security
Linguistic steganography using automatically generated paraphrases

HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
STBS: a statistical algorithm for steganalysis of translation-based steganography

IH'10 Proceedings of the 12th international conference on Information hiding
Blind linguistic steganalysis against translation based steganography

IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Translation-based steganography

IH'05 Proceedings of the 7th international conference on Information Hiding
A New Steganographic Method for Data Hiding in Microsoft Word Documents by a Change Tracking Technique

IEEE Transactions on Information Forensics and Security
Digital Video Steganalysis Exploiting Statistical Visibility in the Temporal Domain

IEEE Transactions on Information Forensics and Security

Jokestega: automatic joke generation-based steganography methodology

International Journal of Security and Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Translation-based steganography (TBS) is a new kind of text steganographic scheme. However, contemporary TBS methods are vulnerable to statistical attacks. Differently, this paper presents a novel TBS, namely Lost in n-best List, abbreviated as LinL, that is resilient against the current statistical attacks. LinL employs only one Statistical Machine Translator (SMT) in the encoding process which selects one of the n-best list of each cover text sentence in order to camouflage messages in stegotext. The presented theoretical analysis demonstrates that there is a classification accuracy upper bound between normal translated text and the stegotext. When the text size is 1000 sentences, the theoretical maximum classification accuracy is about 60%. The experiment results also show current steganalysis methods cannot detect LinL.